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- Title
A Bayesian model for unsupervised detection of RNA splicing based subtypes in cancers.
- Authors
Wang, David; Quesnel-Vallieres, Mathieu; Jewell, San; Elzubeir, Moein; Lynch, Kristen; Thomas-Tikhonenko, Andrei; Barash, Yoseph
- Abstract
Identification of cancer sub-types is a pivotal step for developing personalized treatment. Specifically, sub-typing based on changes in RNA splicing has been motivated by several recent studies. We thus develop CHESSBOARD, an unsupervised algorithm tailored for RNA splicing data that captures "tiles" in the data, defined by a subset of unique splicing changes in a subset of patients. CHESSBOARD allows for a flexible number of tiles, accounts for uncertainty of splicing quantification, and is able to model missing values as additional signals. We first apply CHESSBOARD to synthetic data to assess its domain specific modeling advantages, followed by analysis of several leukemia datasets. We show detected tiles are reproducible in independent studies, investigate their possible regulatory drivers and probe their relation to known AML mutations. Finally, we demonstrate the potential clinical utility of CHESSBOARD by supplementing mutation based diagnostic assays with discovered splicing profiles to improve drug response correlation. RNA splicing variations could help identify cancer subtypes, but this task is computationally challenging. Here, the authors develop CHESSBOARD, a Bayesian tile finding algorithm for splicing data which identifies patterns in the form of tiles and can discover leukemia subgroups associated with therapeutic response.
- Subjects
RNA splicing; MISSING data (Statistics); TILES
- Publication
Nature Communications, 2023, Vol 14, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-022-35369-0